{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T22:54:50Z","timestamp":1789167290486,"version":"build-2803163510"},"reference-count":36,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2016,11,21]],"date-time":"2016-11-21T00:00:00Z","timestamp":1479686400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["www.cambridge.org"],"crossmark-restriction":true},"short-container-title":["Epidemiol. Infect."],"published-print":{"date-parts":[[2017,2]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>Ross River virus (RRV) is a mosquito-borne virus endemic to Australia. The disease, marked by arthritis, myalgia and rash, has a complex epidemiology involving several mosquito species and wildlife reservoirs. Outbreak years coincide with climatic conditions conducive to mosquito population growth. We developed regression models for human RRV notifications in the Mildura Local Government Area, Victoria, Australia with the objective of increasing understanding of the relationships in this complex system, providing trigger points for intervention and developing a forecast model. Surveillance, climatic, environmental and entomological data for the period July 2000\u2013June 2011 were used for model training then forecasts were validated for July 2011\u2013June 2015. Rainfall and vapour pressure were the key factors for forecasting RRV notifications. Validation of models showed they predicted RRV counts with an accuracy of 81%. Two major RRV mosquito vectors (<jats:italic>Culex annulirostris<\/jats:italic>and<jats:italic>Aedes camptorhynchus<\/jats:italic>) were important in the final estimation model at proximal lags. The findings of this analysis advance understanding of the drivers of RRV in temperate climatic zones and the models will inform public health agencies of periods of increased risk.<\/jats:p>","DOI":"10.1017\/s0950268816002594","type":"journal-article","created":{"date-parts":[[2016,11,21]],"date-time":"2016-11-21T03:34:25Z","timestamp":1479699265000},"page":"440-450","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":24,"title":["Predictive modelling of Ross River virus notifications in southeastern Australia"],"prefix":"10.1017","volume":"145","author":[{"given":"Z.","family":"CUTCHER","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"WILLIAMSON","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. 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